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The Impact of Jumps in Volatility and Returns

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  • Eraker, Bjorn
  • Johannes, Michael
  • Polson, Nicholas

Abstract

This paper examines a class of continuous-time models incorporating jumps in returns and volatility, in addition to diffusive stochastic volatility. We develop a likelihood-based estimation strategy and provide estimates of model parameters, spot volatility, jump times and jump sizes using both S&P 500 and Nasdaq 100 index returns. Estimates of jump times, jump sizes and volatility are particularly useful for disentangling the dynamic effects of these factors during periods of market stress, such as those in 1987, 1997 and 1998. Using both formal and informal diagnostics, we find strong evidence for jumps in volatility, even after accounting for jumps in returns. We study the impact of these factors and of estimation risk on option prices.

Suggested Citation

  • Eraker, Bjorn & Johannes, Michael & Polson, Nicholas, 2002. "The Impact of Jumps in Volatility and Returns," Working Papers 02-18, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:02-18
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    Cited by:

    1. Xuemin Yan, 2002. "Valuation of commodity derivatives in a new multi-factor model," Review of Derivatives Research, Springer, vol. 5(3), pages 251-271, October.
    2. Gerlach, Richard & Tuyl, Frank, 2006. "MCMC methods for comparing stochastic volatility and GARCH models," International Journal of Forecasting, Elsevier, vol. 22(1), pages 91-107.
    3. Christoffersen, Peter & Heston, Steve & Jacobs, Kris, 2006. "Option valuation with conditional skewness," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 253-284.
    4. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    5. Xibin Zhang & Maxwell L. King, 2002. "Influence Diagnostics in GARCH Processes," Monash Econometrics and Business Statistics Working Papers 19/02, Monash University, Department of Econometrics and Business Statistics.
    6. Maciej Augustyniak & Alexandru Badescu & Mathieu Boudreault, 2023. "On the Measurement of Hedging Effectiveness for Long-Term Investment Guarantees," JRFM, MDPI, vol. 16(2), pages 1-18, February.

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